
"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase						):
    a   :					int			     =					1







    @register_to_config
    def __init__(      self      :							Dict  ,       __UpperCamelCase      :							Optional[int]=20_00  ,       __UpperCamelCase      :							Optional[int]=0.1  ,       __UpperCamelCase      :							Optional[Any]=20  ,       __UpperCamelCase      :							Optional[int]=1e-3  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = None
        _UpperCAmelCase        = None
        _UpperCAmelCase        = None







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Union[str, torch.device] = None  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = torch.linspace(1  ,       self.config.sampling_eps  ,       __UpperCamelCase  ,       device=__UpperCamelCase  )







    def   _snake_case       (      self      :							Optional[int]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Optional[int]=None  )		->Any:






        '''simple docstring'''






        if self.timesteps is None:
            raise ValueError(
                """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""  )

        # TODO(Patrick) better comments + non-PyTorch
        # postprocess model score
        _UpperCAmelCase        = (
            -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
        )
        _UpperCAmelCase        = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff  )  )
        _UpperCAmelCase        = std.flatten()
        while len(std.shape  ) < len(score.shape  ):
            _UpperCAmelCase        = std.unsqueeze(-1  )
        _UpperCAmelCase        = -score / std

        # compute
        _UpperCAmelCase        = -1.0 / len(self.timesteps  )

        _UpperCAmelCase        = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
        _UpperCAmelCase        = beta_t.flatten()
        while len(beta_t.shape  ) < len(x.shape  ):
            _UpperCAmelCase        = beta_t.unsqueeze(-1  )
        _UpperCAmelCase        = -0.5 * beta_t * x

        _UpperCAmelCase        = torch.sqrt(__UpperCamelCase  )
        _UpperCAmelCase        = drift - diffusion**2 * score
        _UpperCAmelCase        = x + drift * dt

        # add noise
        _UpperCAmelCase        = randn_tensor(x.shape  ,       layout=x.layout  ,       generator=__UpperCamelCase  ,       device=x.device  ,       dtype=x.dtype  )
        _UpperCAmelCase        = x_mean + diffusion * math.sqrt(-dt  ) * noise

        return x, x_mean







    def __len__(      self      :							Any  )		->int:






        '''simple docstring'''






        return self.config.num_train_timesteps

"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a       :						List[Any]       		=		logging.get_logger(__name__)

a       :						str       		=		{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Any			     =					'camembert'







    def __init__(      self      :							Dict  ,       __UpperCamelCase      :							Union[str, Any]=3_05_22  ,       __UpperCamelCase      :							Tuple=7_68  ,       __UpperCamelCase      :							Optional[Any]=12  ,       __UpperCamelCase      :							int=12  ,       __UpperCamelCase      :							int=30_72  ,       __UpperCamelCase      :							int="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							List[Any]=5_12  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Dict=0.0_2  ,       __UpperCamelCase      :							Optional[int]=1e-12  ,       __UpperCamelCase      :							Optional[Any]=1  ,       __UpperCamelCase      :							int=0  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Optional[int]="absolute"  ,       __UpperCamelCase      :							int=True  ,       __UpperCamelCase      :							Dict=None  ,       **__UpperCamelCase      :							List[str]  ,       )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase  ,       bos_token_id=__UpperCamelCase  ,       eos_token_id=__UpperCamelCase  ,       **__UpperCamelCase  )

        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = position_embedding_type
        _UpperCAmelCase        = use_cache
        _UpperCAmelCase        = classifier_dropout







class 		a_    (			_UpperCAmelCase						):







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task == "multiple-choice":
            _UpperCAmelCase        = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _UpperCAmelCase        = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]  )